Mechanistic Modeling of Longitudinal Shape Changes: Equations of Motion and Inverse Problems

This paper examines a longitudinal shape evolution model in which a 3D volume progresses through a family of elastic equilibria in response to the time-derivative of an internal force, or yank, with an additional regularization to ensure diffeomorphic transformations. We consider two different models of yank and address the long time existence and uniqueness of solutions for the equations of motion in both models. In addition, we derive sufficient conditions for the existence of an optimal yank that best describes the change from an observed initial volume to an observed volume at a later time. The main motivation for this work is the understanding of processes such as growth and atrophy in anatomical structures, where the yank could be roughly interpreted as a metabolic event triggering morphological changes. We provide preliminary results on simple examples to illustrate, under this model, the retrievability of some attributes of such events.

[1]  P. Alam ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[2]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

[3]  L. Younes Shapes and Diffeomorphisms , 2010 .

[4]  P. Alam ‘L’ , 2021, Composites Engineering: An A–Z Guide.

[5]  J. Brandt,et al.  Onset and rate of striatal atrophy in preclinical Huntington disease , 2004, Neurology.

[6]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[7]  Alain Trouvé,et al.  The Varifold Representation of Nonoriented Shapes for Diffeomorphic Registration , 2013, SIAM J. Imaging Sci..

[8]  Alain Trouvé,et al.  The Fshape Framework for the Variability Analysis of Functional Shapes , 2014, Found. Comput. Math..

[9]  P. Alam ‘N’ , 2021, Composites Engineering: An A–Z Guide.

[10]  Jane S. Paulsen,et al.  Regional subcortical shape analysis in premanifest Huntington's disease , 2018, Human brain mapping.

[11]  Lena H. Ting,et al.  Yank: the time derivative of force is an important biomechanical variable in sensorimotor systems , 2019, Journal of Experimental Biology.

[12]  Alain Goriely,et al.  Growth and instability in elastic tissues , 2005 .

[13]  Richard S. J. Frackowiak,et al.  How early can we predict Alzheimer's disease using computational anatomy? , 2013, Neurobiology of Aging.

[14]  J. King,et al.  Mathematical modelling of avascular-tumour growth. , 1997, IMA journal of mathematics applied in medicine and biology.

[15]  Norbert Schuff,et al.  Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry , 2011, NeuroImage.

[16]  Laurent Younes,et al.  Normal and Equivolumetric Coordinate Systems for Cortical Areas , 2019, 1911.07999.

[17]  Peter Lorenzen,et al.  Computational Anatomy to Assess Longitudinal Trajectory of Brain Growth , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[18]  Martin Styner,et al.  Hippocampal shape analysis in Alzheimer's disease and frontotemporal lobar degeneration subtypes. , 2012, Journal of Alzheimer's disease : JAD.

[19]  A. Bressan,et al.  A Model of Controlled Growth , 2016, 1608.08645.

[20]  Vlado A. Lubarda,et al.  On the mechanics of solids with a growing mass , 2002 .

[21]  A. Goriely The Mathematics and Mechanics of Biological Growth , 2017 .

[22]  T. R. Hughes,et al.  Mathematical foundations of elasticity , 1982 .

[23]  H. Damasio Human Brain Anatomy in Computerized Images , 1995 .

[24]  P. Thomas Fletcher,et al.  A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis , 2013, IPMI.

[25]  P. Alam,et al.  H , 1887, High Explosives, Propellants, Pyrotechnics.

[26]  Kwame S. Kutten,et al.  3D Normal Coordinate Systems for Cortical Areas , 2018, Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore.

[27]  Michael I. Miller,et al.  Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment , 2018, NeuroImage: Clinical.

[28]  Antonio DiCarlo,et al.  Growth and balance , 2002 .

[29]  Laurent Younes Hybrid Riemannian Metrics for Diffeomorphic Shape Registration , 2018 .

[30]  Michael I. Miller,et al.  Amygdalar atrophy in symptomatic Alzheimer's disease based on diffeomorphometry: the BIOCARD cohort , 2015, Neurobiology of Aging.

[31]  Michael I. Miller,et al.  Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes , 2009, NeuroImage.

[32]  J. Lefévre,et al.  On the growth and form of cortical convolutions , 2016, Nature Physics.

[33]  Jun Ma,et al.  A Bayesian Generative Model for Surface Template Estimation , 2010, Int. J. Biomed. Imaging.

[34]  Jane S. Paulsen,et al.  Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis , 2012, Human brain mapping.

[35]  Alan C. Evans,et al.  BigBrain: An Ultrahigh-Resolution 3D Human Brain Model , 2013, Science.

[36]  Michael I. Miller,et al.  Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer’s Disease , 2019, Front. Aging Neurosci..

[37]  P. Alam,et al.  R , 1823, The Herodotus Encyclopedia.

[38]  P. Alam ‘E’ , 2021, Composites Engineering: An A–Z Guide.

[39]  Michael I. Miller,et al.  A Model for Elastic Evolution on Foliated Shapes , 2018, IPMI.

[40]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[41]  N. Aronszajn Theory of Reproducing Kernels. , 1950 .

[42]  Marie-Gabrielle Vallet,et al.  How to Subdivide Pyramids, Prisms, and Hexahedra into Tetrahedra , 1999, IMR.

[43]  Guido Gerig,et al.  Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data , 2012, International Journal of Computer Vision.

[44]  H. Braak,et al.  Staging of alzheimer's disease-related neurofibrillary changes , 1995, Neurobiology of Aging.